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Research PaperResearchia:202607.24075

Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge

Rodrigo Carmo Terin

Abstract

The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals. The neural and fixed-point solutions agree at the percent level and remain stable under changes of initialization, network size, integration grid, and infrared boundary condition. Variations of the three-gluon vertex model produce substantially larger effects than the neural error. The Min...

Submitted: July 24, 2026Subjects: Machine Learning; Data Science

Description / Details

The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals. The neural and fixed-point solutions agree at the percent level and remain stable under changes of initialization, network size, integration grid, and infrared boundary condition. Variations of the three-gluon vertex model produce substantially larger effects than the neural error. The MiniMOM ultraviolet running and the sign change of the gluon Schwinger function are also reproduced within the limitations of the truncation.


Source: arXiv:2607.21548v1 - http://arxiv.org/abs/2607.21548v1 PDF: https://arxiv.org/pdf/2607.21548v1 Original Link: http://arxiv.org/abs/2607.21548v1

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Date:
Jul 24, 2026
Topic:
Data Science
Area:
Machine Learning
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